I like their description of analysis which they attribute to Allison Rossett:

Analysis is the study we do in order to figure out what to do.

Because that’s exactly what it is. It’s the foundation of all subsequent development activity.

There’s no point launching into a frenzy of work unless you know why you’re doing it. Otherwise your efforts are liable to go to waste, and where’s the value-add in that?

Focus on performance

When conducting a needs analysis in the workplace, it’s important to focus on performance. Not training, not learning, not development… performance.

Your red flag is the Performance Gap, which is the difference between what the level of performance is now, and what it should be.

You need to determine why the gap exists, then design a solution to fix it.

That solution may be training or learning or development, or something else altogether. It may be simple or complex, online or face-to-face, real-time or asynchronous.

It all depends on the nature of the problem.

Data

There are two major approaches to identifying performance gaps:

1. Reactive, and
2. Proactive.

The reactive approach responds to your customer (or someone else) telling you what they need. For example, a Team Leader might call you to say that her team is struggling to meet its productivity targets; or a Project Manager might inform you of the imminent rollout of a new processing system. In either case, you need to react to that information.

While the reactive approach is vitally important, the proactive approach is arguably more important in today’s environment. By adopting a proactive approach, you don’t simply wait for your customer to tell you what their needs are – you find out for yourself.

This kind of analysis relies heavily on data. The data may be subjective, for example:

The data provides the evidence to support what you’re going to do next.

It gives you the confidence that your work will hit the mark and, ultimately, improve the overall performance of the business.

Root cause analysis

When analysing the data, I recommend that you be suspicious, but fair.

For example, if a graph shows that a certain individual is struggling with his productivity score, then yes: suspect that person may be experiencing an issue that’s hindering their performance.

Bear in mind, however, that a myriad of reasons may be influencing the result. Maybe they’re new to the role; maybe they’re sick or burnt out; maybe they’re constantly bombarded all day by their peers. Always consider the conditions and the circumstances.

But at the end of the day, the numbers don’t lie, so you need to do something. Sometimes training is the answer, sometimes it isn’t. Maybe a flawed process needs to be modified; maybe a pod reshuffle is in order; maybe someone just needs a holiday.

Whatever you do, make sure you address the root of the problem, not just the symptoms.